شناسایی و رتبه بندی کاربردهای هوش مصنوعی ارتقادهنده کیفیت خدمات بیمه ای (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
در عصر حاضر تأثیر هوش مصنوعی در همه صنایع ازجمله صنعت بیمه بر کسی پوشیده نیست. بهره گیری از هوش مصنوعی به صنعت بیمه کمک می کند تا خطرات مختلف را پیش بینی کرده و به این ترتیب برنامه های مناسب برای مدیریت آن را اجرا کند. تحقیق حاضر باهدف شناسایی و رتبه بندی کاربردهای هوش مصنوعی ارتقادهنده کیفیت خدمات بیمه ای انجام شد. سازمان تأمین اجتماعی به عنوان موردمطالعه انتخاب شده است. این تحقیق بر اساس هدف کاربردی است و به لحاظ ماهیت و روش از نوع تحقیقات اکتشافی است و ازلحاظ زمانی پژوهش تک مقطعی است. ابتدا با استفاده از ادبیات موضوع کاربردهای مختلف هوش مصنوعی در صنعت بیمه استخراج شده و در گام دوم این داده ها با نظر خبرگان تکمیل شده و در مرحله بعد با استفاده از ابزار پرسشنامه و روش تصمیم گیری چند شاخصه رتبه بندی شاخص ها انجام شده است. برای تحلیل داده ها، ابتدا از روش فار برای وزن دهی شاخص ها استفاده شده، سپس ماتریس تصمیم تشکیل شده و با استفاده از روش مارکوس، گزینه ها رتبه بندی شد. طبق نتایج پژوهش از میان کاربردهای هوش مصنوعی در صنعت بیمه گروه «تحلیل داده ها و پردازش و تجزیه» رتبه یک و «خدمات مشتری» رتبه سه و «امنیت سایبری» و «اتوماسیون» هر دو رتبه چهار را به خود اختصاص دادند. لذا این پژوهش نیز توجه به آماده سازی زمینه برای تحلیل داده های حجیم را برجسته می سازد.Identification and Ranking Applications of AI That Improve the Quality of Insurance Services.
In recent years, artificial intelligence (AI) has emerged as a transformative force across industries, offering unprecedented capabilities in data analysis, process automation, and customer engagement. The insurance sector, inherently data-driven and operationally complex, stands to benefit significantly from AI adoption. While insurers in developed markets have begun to integrate these technologies strategically, organizations in emerging economies like Iran continue to face structural challenges in implementation. This study explores the potential of AI in improving the quality of insurance services within Iran’s Social Security Organization (SSO). Through an empirical model grounded in multi-criteria decision-making, the research identifies twenty-two AI-driven applications and evaluates their impact using expert feedback. By applying FARE (Fuzzy Analytic Relationship) for indicator weighting and MARCOS (Measurement Alternatives and Ranking According to Compromise Solution) for application prioritization, the study offers a structured pathway for digital transformation. The findings suggest that AI applications focused on data analysis and intelligent processing hold the highest potential for service enhancement. In contrast, functions like cybersecurity and customer interaction, while essential, play more supportive roles. This research contributes to the growing body of knowledge by contextualizing AI integration within Iran’s insurance landscape and proposing a practical decision-making framework for public sector innovation. Introduction Artificial intelligence has become more than a technological trend—it is now a cornerstone of innovation in finance, healthcare, manufacturing, and insurance. As organizations contend with increasing volumes of data and rising expectations for personalized, efficient services, AI offers tools that not only automate routine tasks but also augment strategic decision-making. Within the insurance sector, AI applications span a wide array of functionalities, including predictive underwriting, fraud detection, customer service automation, and real-time risk evaluation. These advancements are reshaping insurance from a reactive, compensation-focused industry to a proactive, preventive service model. In Iran, despite visible interest and scattered efforts, the deployment of AI remains unsystematic and largely unstructured. The Social Security Organization, covering more than 45 million individuals, is a prime candidate for AI transformation. Yet the absence of a prioritized roadmap has hindered progress. This study responds to that gap by asking: Which AI applications should the SSO pursue to maximize improvements in service quality? Research Background Globally, the insurance industry has witnessed a surge in AI adoption. Companies such as AXA and Allstate utilize machine learning models to personalize policies and predict claims. Progressive and Lemonade implement chatbots and natural language processing to accelerate customer support. Zurich Insurance leverages robotic process automation to reduce operational latency, while Swiss Re and Munich Re deploy anomaly detection algorithms to prevent fraud and improve pricing accuracy. These examples underscore the strategic role AI plays, not only in optimizing internal processes but also in transforming customer engagement. International literature consistently identifies several application domains: predictive analytics, cybersecurity, automated claims processing, customer personalization, and intelligent document handling. However, within the Iranian context, existing studies have tended to focus narrowly—often examining isolated applications or technologies without offering a comprehensive prioritization framework. Moreover, public insurance institutions lack access to sector-specific evaluations that link technology capabilities to strategic outcomes like customer retention, operational cost reduction, and service satisfaction. This research seeks to bridge that divide by evaluating the functional relevance and strategic value of AI applications from an organizational perspective. Method The study adopts a quantitative and applied research design, supported by a positivist philosophical foundation. It uses a single cross-sectional survey to collect expert judgments and applies a hypothetico-deductive approach for analysis. The sample consists of ten carefully selected professionals affiliated with the SSO, each with proven expertise in insurance and artificial intelligence. These experts evaluated twenty-two performance indicators extracted from a comprehensive review of literature and industry practice. Indicators included operational metrics (e.g., fraud detection efficiency, speed of data processing), service quality measures (e.g., customer satisfaction, claim resolution accuracy), and strategic outcomes (e.g., risk prediction, loss minimization). To quantify the relative importance of each indicator, the FARE method was used. This enabled the assignment of nuanced weights based on expert consensus. Subsequently, each AI application was evaluated using the MARCOS ranking methodology, which integrates weighted scores with ideal and non-ideal performance scenarios to establish priority levels. This hybrid decision-making model ensures that each application is assessed on technical feasibility as well as on its strategic alignment with service enhancement goals. Discussion and Results The analysis revealed clear priorities among the AI applications considered. The domains of data analysis and processing intelligence emerged as the most impactful in improving insurance service quality. These functions directly contribute to accurate risk assessment, efficient pricing, and streamlined claims management—areas that hold the greatest operational and strategic significance for public insurers. Applications such as predictive modeling, historical data mining, and intelligent classification of new information received the highest scores from experts. These tools allow insurance entities to transition from static, rule-based decision-making toward dynamic, data-driven strategies that respond to real-time conditions. In contrast, applications within cybersecurity and customer services, while essential, were deemed supportive rather than primary. Technologies such as chatbots, fraud detection systems, and encryption tools provide safeguards and enhance user experience but rely heavily on underlying analytical systems to deliver consistent value. One of the study’s conceptual contributions lies in distinguishing between direct AI applications and indirect effects, which enhance service through ancillary functions. This distinction has practical implications for implementation, especially in resource-constrained environments where prioritization is critical. Furthermore, the study highlighted a significant gap between global best practices and the current technological posture of Iran’s insurance sector. While international organizations experiment with generative AI and blockchain–AI hybrids, domestic insurers have yet to establish basic integration frameworks. This research, therefore, not only ranks applications but also serves as a blueprint for catching up with global innovation trajectories. Conclusion Artificial intelligence is no longer optional; it’s central to the future of insurance. For Iran's Social Security Organization, strategic investment in AI must begin with data analytics and intelligent processing, which offer the most immediate gains in speed, accuracy, and service quality. Applications in cybersecurity and customer service are also valuable, but their full potential depends on a solid analytical foundation. This study provides more than a ranking; it offers a practical mindset shift, from reactive service delivery to proactive, data-driven decision-making. The proposed framework is adaptable across sectors and scalable with future technological advances. Real success, however, hinges not on algorithms alone, but on how thoughtfully they are embedded into human-centered systems led by vision, collaboration, and continuous learning.
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